Resources for Teaching and Learning with AI
Image by Gerd Altmann from Pixabay
The purpose of this web page is to provide some basic resources for faculty regarding the use of Artificial Intelligence (AI) in teaching and learning. These resources were curated by CTLT staff with their expertise in higher education pedagogy, instructional design, academic technology, writing instruction, and accessibility. The inclusion of any material is not intended to reflect its importance nor is it intended to endorse any views expressed or products or services offered.
We recognize that AI changes rapidly, and we will update this page as we can.
You can also find our current workshop resources relating to AI and other topics in teaching on our Workshops page.
In addition, the CSU Fall Online Course Services Professional Development Training Opportunities are open to all for registration. All courses are 3-weeks asynchronous and facilitated by trained CSU Faculty & Staff.
- Fall Session 2: November 3-23, 2025
- Watch for Winter session information later this quarter!
We hope you will find these resources useful and we welcome your feedback: CTLT@calpoly.edu.
AI in Higher Education
The following resources focus on an overview of AI in higher education. We include two glossaries of common AI terms.
- EDUCAUSE Special Topics | Artificial Intelligence - Provides an overview of AI articles including the use of AI applications in higher education, its promises and perils, ethical implications, and its role in ensuring student success.
- Howard Gardner on the future of AI and Education - a quick read in the Harvard Gazette about the potential future of education
Glossaries of AI terms
- CIRCLS: Glossary of Artificial Intelligence Terms for Educators - Created by the Center for Integrative Research in Computing and Learning Sciences (CIRCLS) for educators to reference when learning about and using AI.
AI Sample Syllabus Statements
The following AI syllabus statements and classroom policies were created by educators at various higher education institutions. Statements include options for no use of AI, limited use, and extensive use, depending on the course and instructor.
- Colorado State: What should a syllabus statement on AI look like?
- University of Minnesota: ChatGPT Syllabus Statements
- Syllabus Statement Builder - Pepperdine's Generative AI Statement (Policy) builder for your syllabus. No AI is involved, and this will assist you in creating the perfect syllabus statement for your course.
AI and Academic Integrity
The following resources address concerns regarding academic integrity when using AI. We include AI citation guidelines.
- Cornell University: AI and Academic Integrity - Provides guidance for instructors to address AI and academic integrity in the classroom.
- Considerations of AI Detection [PDF] - This accessible PDF is based on the original, Considerations of AI Detection - CSU Channel Islands (Google Slides) - From CSU Channel Islands Teaching and Learning Innovations, provides an overview of AI detection tools and their limitations.
AI Citation Guidelines
The following resources provide guidelines for citing and referencing ChatGPT and other AI tools using the style guide appropriate to your discipline:
AI and Bias
The following resources provide an overview of bias in AI systems. AI bias refers to the tendency of algorithms to reflect human biases, since output is based on human-generated AI training data and reflects historical and social inequities.
- The Radical AI Podcast: More than a Glitch, Technochauvanism, and Algorithmic Accountability with Meredith Broussard - An interview with Meredith Broussard, author of More than a Glitch.
- Penn State: AI language models show bias against people with disabilities - Describes findings from a Penn State study showing that all the algorithms and models they tested contained significant implicit bias against people with disabilities.
Books foundational to AI Bias research
- Race After Technology - Ruha Benjamin - Benjamin examines various technologies, ranging from common apps to complicated algorithms, and explores how these new technologies can reinforce White supremacy and make social inequality worse.
- Algorithms of Oppression - Safiya Umoja Noble - Noble describes data discrimination as a real social problem. She argues that search algorithms are biased, privilege whiteness, and discriminate against people of color, specifically women of color.
- More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech - Meredith Broussard - Broussard argues that racism, sexism, and ableism aren't just “glitches” in well-functioning AI systems; she argues they are coded into the systems themselves.
AI Ethical Concerns and Challenges
The following resources provide an overview of ethical concerns and challenges regarding AI systems and their use, including privacy and security, copyright and data ownership, inaccuracies in output, and more.
- Santa Clara University: Artificial Intelligence and Ethics: Sixteen Challenges and Opportunities - Provides a description of 16 ethical concerns regarding AI use and development, with links to further resources.
- Generative AI and Professional Ethics : Amanda Sturgill of the Center for Engaged Learning at Elon University explores AI and Ethics from the perspective of a Journalism professor.
- IBM: What is AI Ethics? - Provides a comprehensive view of AI ethics, including principles, primary concerns, possible solutions, and links to further resources.
Teaching with AI
The following resources provide examples of teaching with AI in the higher education classroom.
- (Re)thinking Assessments with AI in Mind - a checklist by Keli Yerian of OSU's College of Arts and Sciences, and published under Creative Commons
- OSU: AI: Considerations for Teaching and Learning - Created by Ohio State University, provides an overview of Generative AI benefits and limitations, as well as teaching strategies and examples.
- Understanding AI Writing Tools and Their Uses for Teaching and Learning at UC Berkeley - Includes an overview of ChatGPT, teaching and syllabus statement recommendations, and suggested writing prompts and activities.
- Artificial Intelligence Tools: Bloom's Taxonomy Revisited - Created by Oregon State University, provides a revised version of Bloom's Taxonomy incorporating the use of AI, and provides examples.
AI and Digital Literacy
The following resources provide guidance on building AI literacy skills, including understanding how technologies like machine learning and generative AI work, how they can be used for problem-solving, and the technology’s consequences.
- Critical Media Literacy Guides - Derived from Critical Media Literacy and Civic Learning. Includes a section, Teacher and Student Guide to Analyzing AI Writing Tools, that provides a list of analysis questions regarding the AI tools themselves, as well as questions about the text produced by the tools.
- TextGenEd: An Introduction to Teaching with Text Generation Technologies - Created by the Writing Across the Curriculum (WAC) Clearinghouse - Provides a collection of assignments focused on AI Literacy development. The collection is accessible to teachers with different levels of comfort using technologies.
- Supporting AI Literacy for Educators: New and Emerging Resources - Created by Digital Promise, provides resources to support educators in developing AI literacies.
- Cornell Center for Teaching Innovation: Ethical AI for Teaching and Learning - Guidance on building literacy in Generative AI including understanding, evaluating, and becoming familiar with the uses of generative AI tools.
CSU AI Commons
This site is the ultimate resource for Tools and Information for CSU Students, Faculty, Staff, and Alumni.
